Existing Performance Analysis Tools Research Articles

A Minimally Intrusive Approach for Automatic Assessment of Parallel Performance Scalability of Shared-Memory HPC Applications

High-performance computing systems have become increasingly dynamic, complex, and unpredictable. To help build software that uses full-system capabilities, performance measurement and analysis tools exploit extensive execution analysis focusing on single-run results. Despite being effective in identifying performance hotspots and bottlenecks, these tools are not sufficiently suitable to evaluate the overall scalability trends of parallel applications. Either they lack the support for combining data from multiple runs or collect excessive data, causing unnecessary overhead. In this work, we present a tool for automatically measuring and comparing several executions of a parallel application according to various scenarios characterized by the input arrangements, the number of threads, number of cores, and frequencies. Unlike other existing performance analysis tools, the proposed work covers some gaps in specialized features necessary to better understand computational resources scalability trends across configurations. In order to improve scalability analysis and productivity over the vast spectrum of possible configurations, the proposed tool features automatic instrumentation, direct mapping of parallel regions, accuracy-preserving data reductions, and ease of use. As it aims at accurately understanding scalability trends of parallel applications, detailed single-run performance analyses show minimal intrusion (less than 1% overhead).

On Haskell and energy efficiency

BackgroundRecent work has studied diverse affecting factors on software energy efficiency. ObjectiveThis paper attempts to shed light on the energy behavior of programs written in a lazy, purely functional programming language, Haskell. MethodologyWe conducted two in-depth and complementary studies to analyze the energy efficiency of programs from two different perspectives: strictness and concurrency. ResultsWe found that small changes can make a big difference. In one benchmark, under a specific configuration, choosing the MVar data sharing primitive over TMVar can yield 60% energy savings. In another benchmark, TMVar can yield up to 30% savings over MVar. Thus, tools that support developers in refactoring a program to switch between primitives can be very useful. In addition, the relationship between energy consumption and performance is not always clear. In sequential benchmarks, high performance is an accurate proxy for low energy consumption. However, for one of our concurrent benchmarks, the variants with the best performance also exhibited the worst energy consumption. We report on deviating cases. ConclusionsTo support developers, we have extended existing performance analysis tools to also gather and present data about energy consumption. Furthermore, we provide a set of guidelines to help Haskell developers save energy.

Grain graphs

Average programmers struggle to solve performance problems in OpenMP programs with tasks and parallel for-loops. Existing performance analysis tools visualize OpenMP task performance from the runtime system's perspective where task execution is interleaved with other tasks in an unpredictable order. Problems with OpenMP parallel for-loops are similarly difficult to resolve since tools only visualize aggregate thread-level statistics such as load imbalance without zooming into a per-chunk granularity. The runtime system/threads oriented visualization provides poor support for understanding problems with task and chunk execution time, parallelism, and memory hierarchy utilization, forcing average programmers to rely on experts or use tedious trial-and-error tuning methods for performance. We present grain graphs , a new OpenMP performance analysis method that visualizes grains -- computation performed by a task or a parallel for-loop chunk instance -- and highlights problems such as low parallelism, work inflation and poor parallelization benefit at the grain level. We demonstrate that grain graphs can quickly reveal performance problems that are difficult to detect and characterize in fine detail using existing visualizations in standard OpenMP programs, simplifying OpenMP performance analysis. This enables average programmers to make portable optimizations for poor performing OpenMP programs, reducing pressure on experts and removing the need for tedious trial-and-error tuning.

New union bound on the error probability of bit-interleaved space–time codes with finite interleaver sizes

A new union bound on the bit error probability of bit-interleaved space–time (BI-ST) coded systems is derived. Unlike existing performance analysis tools for BI-ST systems, the new bound provides a general framework for analysing the performance of BI-ST systems employing finite interleaver sizes. The derivation is based on the uniform interleaving assumption of the coded sequence prior to transmission over multiple antennas. The new bound is a function of the distance spectrum of the code, the signal constellation used and the space–time (ST) mapping scheme. The bound is derived for a general BI-ST coded system and applied to two specific examples, namely, the BI space–time coded modulation and the BI space–time block codes. Results show that the analysis provides a close approximation to the BI-ST performance for a wide range of signal-to-noise ratios. The analysis can also accurately characterise the performance differences between different interleaver sizes, which is a breakthrough in the analysis of BI-ST coded systems.

SCALEA: a performance analysis tool for parallel programs

AbstractMany existing performance analysis tools lack the flexibility to control instrumentation and performance measurement for code regions and performance metrics of interest. Performance analysis is commonly restricted to single experiments.In this paper we present SCALEA, which is a performance instrumentation, measurement, analysis, and visualization tool for parallel programs that supports post‐mortem performance analysis. SCALEA currently focuses on performance analysis for OpenMP, MPI, HPF, and mixed parallel programs. It computes a variety of performance metrics based on a novel classification of overhead. SCALEA also supports multi‐experiment performance analysis that allows one to compare and to evaluate the performance outcome of several experiments. A highly flexible instrumentation and measurement system is provided which can be controlled by command‐line options and program directives. SCALEA can be interfaced by external tools through the provision of a full Fortran90 OpenMP/MPI/HPF frontend that allows one to instrument an abstract syntax tree at a very high‐level with C‐function calls and to generate source code. A graphical user interface is provided to view a large variety of performance metrics at the level of arbitrary code regions, threads, processes, and computational nodes for single‐ and multi‐experiment performance analysis. Copyright © 2003 John Wiley & Sons, Ltd.